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Alison Doherty and Graham Cuskelly

detected. We also reviewed collinearity statistics prior to the interpretation of the regression results; tolerance values of 0.01 or less and variance inflation factor scores over 10.0 would be indicative of multicollinearity among the variables ( Tabachnik & Fidell, 2012 ). Results Organizational

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Cornelia Frank, Taeho Kim and Thomas Schack

-Bonferroni correction was employed in order to account for the inflation of type I errors ( Holm, 1979 ). Cohen’s d was used as an estimate of effect size ( Cohen, 1992 ). Results Representation of the Putt Mean group dendrograms are displayed in Figures  1A and 1B . For each dendrogram, the numbers on the x -axis

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Mindy Patterson, Wanyi Wang and Alexis Ortiz

females, respectively. The residuals in the dependent variable were approximately normally distributed. Multicollinearity assumptions using tolerance and variance inflation factor (VIF) were also tested. In the present study, tolerance < 0.10 or VIF > 10 was considered multicollinearity. The following

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Jakob Tarp, Anna Bugge, Niels Christian Møller, Heidi Klakk, Christina Trifonov Rexen, Anders Grøntved and Niels Wedderkopp

indication of problematic collinearity was detected (variance inflation factors did not exceed 1.92) and estimates were robust to removal of observations with standardized residuals above/below 2 SD. As the primary outcome we created a standardized composite score 25 of the cardiometabolic risk factors

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Avelina C. Padin, Charles F. Emery, Michael Vasey and Janice K. Kiecolt-Glaser

met. Specifically, visual inspection of residual plots of regression models indicated that the regression assumption of homoscedasticity was met. Calculation of the variance inflation factor and tolerance indicated that multicollinearity was not present. Results Attitudes and Average PA Duration

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Hotaka Maeda, Chris C. Cho, Young Cho and Scott J. Strath

note, Oliver, Schluter, and Schofield ( 2011 ) introduced a similar method that modeled minute-level accelerometer data. However, this method was not included in the study as Lee and Gill’s ( 2018 ) method was able to take into account more complex data properties including the zero-inflation and

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Nadia Cristina Valentini, Glauber Carvalho Nobre, Mariele Santayana de Souza and Michael J. Duncan

variance inflation factor was used to investigate the multicollinearity of the data, with values >10 considered an indicator of multicollinearity 39 ; P  < .05 considered as significant. Results Outliers were not detected in the Mahalanobis squared distance test. Furthermore, the results obtained from the

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Fleur E.C.A. van Rens, Rebecca A. Ashley and Andrea R. Steele

and athletic identities and the student-athletes’ GPAs. Collinearity was not a concern in the analysis, as the variance inflation factor was 1.0 ( Hair, Anderson, Tatham, & Black, 1998 ). Together, the student-athletes’ academic and athletic identities explained 16.3% of the variance in their GPAs, F

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Masakazu Matsuoka, Hiroshi Kunimura and Koichi Hiraoka

effect and multiple comparison tests were repeated many times. Therefore, we cannot rule out the increase in type I error due to too many repeated tests causing probability inflation and the increase in type II error due to too much alpha adjustment. This limitation may have caused overestimation or

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Stephen Hunter, Andrei Rosu, Kylie D. Hesketh, Ryan E. Rhodes, Christina M. Rinaldi, Wendy Rodgers, John C. Spence and Valerie Carson

characteristics. Continuous variables were checked for outliers (exceeding ± 3 SDs) and truncated where necessary. Multicollinearity was assessed among independent variables by checking variance inflation factors. For the main objective, simple linear regressions were first performed using the MIXED procedure